arXiv subjects

Shuyuan Zheng

Publications and source records attributed to Shuyuan Zheng.

24 records · Page 2Linked to original sources

arXiv2024

Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)

The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of a parameter server. However, existing SV calculation methods for FL assume that the server can access the raw FL models and public test data. This may not be a valid assumption in practice considering the emerging privacy attacks on FL models and the fact that test data might be clients' private assets. Hence, we investigate the problem of secure SV calculation for cross-silo FL. We first propose HESV, a one-server solution based solely on homomorphic encryption (HE) for privacy protection, which has limitations in efficiency. To overcome these limitations, we propose SecSV, an efficient two-server protocol with the following novel features. First, SecSV utilizes a hybrid privacy protection scheme to avoid ciphertext--ciphertext multiplications between test data and models, which are extremely expensive under HE. Second, an efficient secure matrix multiplication method is proposed for SecSV. Third, SecSV strategically identifies and skips some test samples without significantly affecting the evaluation accuracy. Our experiments demonstrate that SecSV is 7.2-36.6 times as fast as HESV, with a limited loss in the accuracy of calculated SVs.

cs.CR↗

arXiv2024

Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents

Large Language Models (LLMs) have increasingly been utilized in social simulations, where they are often guided by carefully crafted instructions to stably exhibit human-like behaviors during simulations. Nevertheless, we doubt the necessity of shaping agents' behaviors for accurate social simulations. Instead, this paper emphasizes the importance of spontaneous phenomena, wherein agents deeply engage in contexts and make adaptive decisions without explicit directions. We explored spontaneous cooperation across three competitive scenarios and successfully simulated the gradual emergence of cooperation, findings that align closely with human behavioral data. This approach not only aids the computational social science community in bridging the gap between simulations and real-world dynamics but also offers the AI community a novel method to assess LLMs' capability of deliberate reasoning.

cs.AI↗

arXiv2023

Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations

Computer simulations offer a robust toolset for exploring complex systems across various disciplines. A particularly impactful approach within this realm is Agent-Based Modeling (ABM), which harnesses the interactions of individual agents to emulate intricate system dynamics. ABM's strength lies in its bottom-up methodology, illuminating emergent phenomena by modeling the behaviors of individual components of a system. Yet, ABM has its own set of challenges, notably its struggle with modeling natural language instructions and common sense in mathematical equations or rules. This paper seeks to transcend these boundaries by integrating Large Language Models (LLMs) like GPT into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based Modeling (SABM). Building upon the concept of smart agents -- entities characterized by their intelligence, adaptability, and computation ability -- we explore in the direction of utilizing LLM-powered agents to simulate real-world scenarios with increased nuance and realism. In this comprehensive exploration, we elucidate the state of the art of ABM, introduce SABM's potential and methodology, and present three case studies (source codes available at https://github.com/Roihn/SABM), demonstrating the SABM methodology and validating its effectiveness in modeling real-world systems. Furthermore, we cast a vision towards several aspects of the future of SABM, anticipating a broader horizon for its applications. Through this endeavor, we aspire to redefine the boundaries of computer simulations, enabling a more profound understanding of complex systems.

cs.AI↗

arXiv2023

FL-Market: Trading Private Models in Federated Learning

The difficulty in acquiring a sufficient amount of training data is a major bottleneck for machine learning (ML) based data analytics. Recently, commoditizing ML models has been proposed as an economical and moderate solution to ML-oriented data acquisition. However, existing model marketplaces assume that the broker can access data owners' private training data, which may not be realistic in practice. In this paper, to promote trustworthy data acquisition for ML tasks, we propose FL-Market, a locally private model marketplace that protects privacy not only against model buyers but also against the untrusted broker. FL-Market decouples ML from the need to centrally gather training data on the broker's side using federated learning, an emerging privacy-preserving ML paradigm in which data owners collaboratively train an ML model by uploading local gradients (to be aggregated into a global gradient for model updating). Then, FL-Market enables data owners to locally perturb their gradients by local differential privacy and thus further prevents privacy risks. To drive FL-Market, we propose a deep learning-empowered auction mechanism for intelligently deciding the local gradients' perturbation levels and an optimal aggregation mechanism for aggregating the perturbed gradients. Our auction and aggregation mechanisms can jointly maximize the global gradient's accuracy, which optimizes model buyers' utility. Our experiments verify the effectiveness of the proposed mechanisms.

cs.LG↗

arXiv2021

Pricing Private Data with Personalized Differential Privacy and Partial Arbitrage Freeness

There is a growing trend regarding perceiving personal data as a commodity. Existing studies have built frameworks and theories about how to determine an arbitrage-free price of a given query according to the privacy loss quantified by differential privacy. However, those studies have assumed that data buyers can purchase query answers with the arbitrary privacy loss of data owners, which may not be valid under strict privacy regulations and data owners' increasing privacy concerns. In this paper, we study how to empower data owners to control privacy loss in data trading. First, we propose a framework for trading personal data that enables data owners to bound their personalized privacy losses. Second, since bounded privacy losses indicate bounded utilities of query answers, we propose a reasonable relaxation of arbitrage freeness named partial arbitrage freeness, i.e., the guarantee of arbitrage-free pricing only for a limited range of utilities, which provides more possibilities for our market design. Third, to avoid arbitrage, we propose a general method for ensuring arbitrage freeness under personalized differential privacy. Fourth, to fully utilize data owners' personalized privacy loss bounds, we propose privacy budget allocation techniques to allocate privacy losses for queries under arbitrage freeness. Finally, we conduct experiments to verify the effectiveness of our proposed trading protocols.

cs.CR↗

arXiv2019

Trading Location Data with Bounded Personalized Privacy Loss

As personal data have been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Although some people are willing to trade their personal data for money, they might still expect limited privacy loss, and the maximum tolerable privacy loss varies with each individual. In this paper, we propose a framework that enables individuals to trade their personal data with bounded personalized privacy loss, which raises technical challenges in the aspects of budget allocation and arbitrage-freeness. To deal with those challenges,we propose two arbitrage-free trading mechanisms with different advantages.

cs.CR↗